We've touched on the use of vector databases as we've started to explore how LLMs and conversational AIs can be useful, but what are they and how do they work? How are they used for more than just LLMs? Mark and Allen explore some of the classic vector DBs, such as HNSW, and some of the newer fully managed ones, including Metal and Pinecone. We even start to ponder what a fully managed embedding and vector db system might look like from the likes of Google, Azure, or AWS, and are surprised that we're closer than we thought!

Resources:

* HNSWlib: https://github.com/nmslib/hnswlib

* Pinecone: https://pinecone.io/

* Metal: https://getmetal.io/

* Google Cloud Vertex AI Matching Engine: https://cloud.google.com/vertex-ai/docs/matching-engine/overview

* Amazon AWS Bedrock: https://aws.amazon.com/blogs/machine-learning/announcing-new-tools-for-building-with-generative-ai-on-aws/